Evidence-Based Guidelines for Prehospital Airway Management: Methods and Resources Document
Bibliographic record
Abstract
INTRODUCTION: Emergency airway management is a common and critical task EMS clinicians perform in the prehospital setting. A new set of evidence-based guidelines (EBG) was developed to assist in prehospital airway management decision-making. We aim to describe the methods used to develop these EBGs. METHODS: The EBG development process leveraged the four key questions from a prior systematic review conducted by the Agency for Healthcare Research and Quality (AHRQ) to develop 22 different population, intervention, comparison, and outcome (PICO) questions. Evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework and tabulated into the summary of findings tables. The technical expert panel then used a rigorous systematic method to generate evidence to decision tables, including leveraging the PanelVoice function of GRADEpro. This process involved a review of the summary of findings tables, asynchronous member judging, and online facilitated panel discussions to generate final consensus-based recommendations. RESULTS: The panel completed the described work product from September 2022 to April 2023. A total of 17 summary of findings tables and 16 evidence to decision tables were generated through this process. For these recommendations, the overall certainty in evidence was "very low" or "low," data for decisions on cost-effectiveness and equity were lacking, and feasibility was rated well across all categories. Based on the evidence, 16 "conditional recommendations" were made, with six PICO questions lacking sufficient evidence to generate recommendations. CONCLUSION: The EBGs for prehospital airway management were developed by leveraging validated techniques, including the GRADE methodology and a rigorous systematic approach to consensus building to identify treatment recommendations. This process allowed the mitigation of many virtual and electronic communication confounders while managing several PICO questions to be evaluated consistently. Recognizing the increased need for rigorous evidence evaluation and recommendation development, this approach allows for transparency in the development processes and may inform future guideline development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.209 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.034 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.044 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".